Methods for predicting risk of metastasis in cutaneous melanoma

ABSTRACT

The invention as disclosed herein in encompasses a method for predicting the risk of metastasis of a primary cutaneous melanoma tumor, the method encompassing measuring the gene-expression levels of at least eight genes selected from a specific gene set in a sample taken from the primary cutaneous melanoma tumor; determining a gene-expression profile signature from the gene expression levels of the at least eight genes; comparing the gene-expression profile to the gene-expression profile of a predictive training set; and providing an indication as to whether the primary cutaneous melanoma tumor is a certain class of metastasis or treatment risk when the gene expression profile indicates that expression levels of at least eight genes are altered in a predictive manner as compared to the gene expression profile of the predictive training set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/783,755, filed Mar. 14, 2013, the disclosure of which is explicitlyincorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION

Cutaneous melanoma (CM) is an aggressive form of cancer presenting over76,000 diagnosed cases in 2012.¹ CM tumors develop through a number ofdiscreet stages during the progression from a benign melanocytic nevusto a malignant metastatic tumor. Generally, benign nevi present as thin,pigmented lesions. After the acquisition of key genetic mutations andthe initiation of cytoarchitectural modifications leading to shallowinvasion of the skin, the lesions begin growing radially, a processreferred to as the radial growth phase. Upon escape from growth controlmediated by surrounding keratinocytes, stromal invasion to deeperregions of the dermis occurs, marking the progression to the verticalgrowth phase. The vertical growth phase, along with the geneticalterations that accompany this process, is thought to be the criticalstepping stone in the development of metastatic melanoma.

As is common in many other cancers, if CM is detected in the earlystages of tumor progression and appropriately treated, then a long-termmetastasis free and high overall survival following diagnosis is likelyfor the majority of patients.^(2,3) For example, subjects diagnosed withlow risk, stage I CM tumors have a 5-year overall survival rate of91-97%.³ A number of histological factors are used to stage CM and areassociated with prognosis. These factors include Breslow thickness,mitotic index, ulceration, and spread of disease from the primary tumorto sentinel and/or regional lymph nodes.³⁻⁷ Tumor stage is determinedbased upon these histopathological parameters using the well-known TNM(T=primary tumor, N=regional lymph nodes, M=distant metastases) systemthat defines stages 0-IV.³ The TNM staging system is highly accurate formetastasis-free survival for stage 0, in situ, melanomas that do notinvade the dermal layer (5-year survival of 99%), and stage IV melanomas(5-year survival<10%), in which distant metastasis was detected at thetime of primary diagnosis. Metastasis and short-term survival has beendocumented for subjects with stage I disease, with 5-10% of stage Itumors reporting metastatic activity despite successful surgicalintervention (wide local excision with adequate clear margins). So,while the majority of patients with clinical stage I disease have a lowchance of metastasis risk disease some patients will develop metastaticdisease.

Prognosis for clinical stage II and stage III cases has poor accuracy asthere is a large range within each stage and a larger overlap betweenthe stages in 5-year survival rates. Under the current staging system,the 5-year survival rate for clinical stage II subjects is 53-82%, whilethe stage III 5-year survival is rate 22-68%.^(3,8) The distinguishinghallmark between clinical stage II and stage III tumors, and stage I andstage III tumors, is the presence of localized metastasis of CM cells inthe sentinel lymph node (SLN) following a SLN biopsy procedure. Patientswith a positive SLN are clinically upstaged to stage III. However, highfalse negative rates and disease recurrence are associated withhistological analysis of SLNs as evidenced by the wide ranges ofmetastatic free survival and overall survival in stage II and IIIpatients. Immunohistochemical and genetic amplification techniquesdesigned to improve the common hematoxylin and eosin staining methodsfor detection of regional disease have been developed, but only providemarginal improvements^(9,10). Biomarkers have been identified in SLNtissue, and analyzed to improve the ability to recognize CM cells inSLNs, however, these methods have shown limited improvement in accuracyand are compounded by extensive tissue sampling from invasive biopsy ofthe lymph node and have not been adopted for clinical use.^(6,11) Inaddition, melanomas can enter the blood directly by intravasating intovenous capillaries. Thus, the low sensitivity of SLN biopsy may relateto a direct hematogenous metastatic event versus an inaccurate SLNbiopsy result. Regardless of the clinical deficiencies observed for theSLNB procedure, it remains the single best prognostic factor forpredicting metastatic risk for CM patients, and is included in multipleassociation guidelines for clinical management of CM disease.

Inaccurate prognosis for metastatic risk has profound effects uponpatients that are treated according to a population approach rather thanan individual or personalized approach. For example, CM patientscategorized as stage III through the use of current histologicaltechniques, but who have an actual individual risk of metastasis that islow (false positive), are inappropriately exposed to over-treatment thatincludes enhanced surveillance, nodal surgery, and chemotherapy.¹²Similarly, who are SLN negative, that is no CM cells were found withinthe SLN's, remainstage I or II disease but who actually have a high riskfor metastasis (false negative), are at risk of under-treatment. Inaddition, SLN biopsy is an intervention that typically occurs undergeneral anesthesia, exposes patients to significant clinicalcomplications, and has a low positivity rate. For example, NationalComprehensive Cancer Network (NCCN) Guidelines® currently recommend thatpatients with CM staged at 1b (Breslow's thickness≧0.75 mm but <1.00 mmor presence of ≧1 mitosis at any Breslow's thickness) are recommended toundergo SLN biopsy yet only 5% of SLN yield positivity. Meaning that of20 patients with stage 1b melanomas who undergo SLN biopsy, 19 will benegative and exposed to a surgical complication of SLN biopsy.¹²Similarly, all pathologic stage II patients (Breslow's thickness>1.0 mm)are recommended to undergo SLN biopsy yet only 18% will have a positiveSLN.^(12,13) In addition, a positive SLN biopsy results inrecommendations for a complete regional lymph dissection which exposespatients to significant clinical complications, such as lymphedema, andhas a low positivity rate.

To this end, gene expression profile (GEP) signatures have beendeveloped and some have been shown to have powerful prognosticcapabilities in a number of malignant diseases¹⁴⁻¹⁸. One such signaturehas been used for prognostication of uveal melanoma, a tumor ofmelanocytic origin that develops in the eye. Like cutaneous melanoma,treatment of the primary uveal tumor is highly effective. Two to fourpercent of uveal melanoma patients present with evidence of clinicalmetastasis at the time of diagnosis, yet up to 50% of uveal melanomapatients develop systemic metastases within five years of diagnosisregardless of primary eye tumor treatment (radiation therapy orenucleation)¹⁹. This means that a micrometastatic event has occurred inapproximately 50% of uveal melanoma patients prior to primary eye tumortreatment. A GEP signature has recently been developed that canaccurately distinguish uveal melanoma tumors that have a low risk ofmetastasis from those that have a high risk^(14,20). To assess geneticexpression real-time polymerase chain reaction (RT-PCR) analysis isperformed for fifteen genes (twelve discriminating genes and threecontrol genes) that are differentially expressed in tumors with knownmetastatic activity compared to tumors with no evidence of metastasis.The uveal melanoma gene signature separates cases into a low risk groupthat has greater than 95% metastasis free survival five years afterdiagnosis, and a high risk group with less than 20% metastasis freesurvival at the same time point. The signature has been extensivelyvalidated in the clinical setting, and has been shown to provide asignificant improvement in prognostic accuracy compared toclassification by TNM staging criteria^(20,21).

A number of groups have published genomic analysis of tumors incutaneous melanoma²²⁻²⁹. While some studies have focused on the geneticalterations in malignant melanoma cells compared to normal melanocytes,others have compared benign nevi to tumors in the radial or verticalgrowth phases, or primary tumor to metastatic tumors. At the time thestudies contained within this patent were designed and implemented(2010), no evidence could be found in the literature or other publicdomain sources that indicated a gene expression profile test focusedsolely on the primary melanoma tumor could be developed for the clinicalapplication of predicting metastasis in patients with CM.^(22-24,27-29)In addition, all studies utilized fresh frozen CM samples rather thanformalin fixed paraffin embedded (FFPE) tumor tissue. All of the studiesrelated to this invention have only used FFPE primary tumor tissue.

SUMMARY OF THE INVENTION

There is a need in the art for a more accurate and objective method ofpredicting which tumors display aggressive metastatic activity.Development of an accurate molecular footprint, such as the geneexpression profile assay encompassed by the invention disclosed herein,by which CM metastatic risk could be assessed from primary tumor tissuewould be a significant advance forward for the field. Inaccurateprognosis for metastatic risk has profound effects upon patientsincluding inappropriate exposure to over-treatment that includesenhanced surveillance, nodal surgery, and chemotherapy. Patients withinaccurate diagnoses are also at risk of under-treatment; that is,cancer cells are not seen in the sentinel lymph node although they arepresent and may have already spread to other regional lymph nodes orother parts of the body. A false-negative biopsy result gives thepatient and the doctor a false sense of security about the extent ofcancer in the patient's body. In addition, SLN biopsy exposes patientsto significant clinical complications, such as lymphedema, and has a lowpositivity rate.

In one embodiment, the invention as disclosed herein is a method forpredicting risk of metastasis, overall survival, or both, in a patientwith a primary cutaneous melanoma tumor, the method comprising: (a)measuring gene expression levels of at least eight genes selected fromthe group consisting of BAP1_varA, BAP1_varB, MGP, SPP1, CXCL14, CLCA2,S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B, S100A9, CRABP2,KRT14, ROBOT, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL,LTA4H, and CST6, in a sample of the primary cutaneous melanoma tumor,wherein measuring gene expression levels of the at least eight genescomprises measurement of a level of fluorescence by a sequence detectionsystem following RT-PCR of the at least eight genes; (b) determining apatient gene-expression profile signature comprising the gene expressionlevels of the at least eight genes; (c) comparing the patientgene-expression profile signature to a gene-expression profile of apredictive training set; and (d) providing an indication as to a risk ofmetastasis, overall survival or both for the primary cutaneous melanomatumor when the patient gene expression profile indicates that theexpression levels of at the least eight genes are altered in apredictive manner as compared to the gene expression profile of thepredictive training set.

This disclosure provides a more objective method that more accuratelypredicts which melanoma tumors display aggressive metastatic activityand result in decreased patient overall survival. Development of anaccurate molecular footprint, such as the gene expression profile assayencompassed by the invention disclosed herein, by which CM metastaticrisk and patient overall survival could be assessed from primary tumortissue would be a significant advance forward for the field leading todecreased loss of life, less patient suffering, more efficienttreatments and use of resources.

In another embodiment, the invention as disclosed herein is a method fora method of treating cutaneous melanoma in a patient, the methodcomprising: (a) measuring gene expression levels of at least eight genesselected from the group consisting of BAP1_varA, BAP1_varB, MGP, SPP1,CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B,S100A9, CRABP2, KRT14, ROBOT, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29,AQP3, TYRP1, PPL, LTA4H, and CST6 in a sample of a primary cutaneousmelanoma tumor in the patient, wherein measuring gene expression levelsof the at least eight genes comprises determining a level offluorescence by a sequence detection system following RT-PCR of the atleast eight genes; (b) determining a patient gene-expression profilesignature comprising the gene expression levels of the at least eightgenes; (c) comparing the patient gene-expression profile signature to agene-expression profile of a predictive training set; (d) making adetermination as to whether the patient gene-expression profilesignature of the at least eight genes is altered in a predictive manner;and (e) administering an aggressive cancer treatment regimen to thepatient when the determination is made in the affirmative that thepatient has a primary cutaneous melanoma tumor with an increased risk ofmetastasis or decreased overall survival.

In an additional embodiment, the invention as disclosed herein is amethod of treating cutaneous melanoma in a patient, the methodcomprising: (a) measuring gene expression levels of at least eight genesselected from the group consisting of BAP1_varA, BAP1_varB, MGP, SPP1,CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B,S100A9, CRABP2, KRT14, ROBOT, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29,AQP3, TYRP1, PPL, LTA4H, and CST6 in a sample of a primary cutaneousmelanoma tumor in the patient, wherein measuring gene expression levelsof the at least eight genes comprises determining a level offluorescence by a sequence detection system following RT-PCR of the atleast eight genes; (b) determining a patient gene-expression profilesignature comprising the gene expression levels of the at least eightgenes; (c) comparing the patient gene-expression profile signature to agene-expression profile of a predictive training set; (d) making adetermination as to whether the patient gene-expression profilesignature of the at least eight genes is altered in a predictive manner;and (e) performing a sentinel lymph node biopsy (SLNB) on the patientwhen the determination is made in the affirmative that the patient has aprimary cutaneous melanoma tumor with an increased risk of metastasis ordecreased overall survival.

Specific embodiments of the invention will become evident from thefollowing more detailed description of certain embodiments and theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts statistical analysis and graphs illustrating Kaplan-Meier(K-M) analysis of metastasis free survival (MFS) for CM cases predictedto be at low-risk (class 1) or high-risk (class 2) of metastasisaccording to a radial basis machine (RBM) modeling algorithm. 5-yr MFSfor the 164 sample training set (A) is 91% for class 1 cases and 25% forclass 2 cases (p<0.0001). 5-yr MFS for the independent 104 samplevalidation set (B) is 97% for class 1 compared to 31% for class 2(p<0.0001). Accuracy of the training set model, as measured by ROC, is0.9052, compared to 0.9089 for the validation cohort, each reflectingclinically valuable models.

FIG. 2 depicts statistical and K-M analysis of the 164 sample trainingset when metastasis is predicted using partition tree (A), K-nearestneighbor (B), logistic regression (C), or discriminant analysis (D)modeling algorithms. Highly significant differences in K-M MFS curvesfor class 1 and class 2 cohorts are observed with each modeling method.Accuracy is highest when analysis is performed with the partition treemodel (ROC=0.9176; accuracy=87%; sensitivity=94%), while the othermodels are also statistically accurate, with ROC>0.8.

FIG. 3 depicts the same statistics and graphs as FIG. 2, but withpartition tree (A), K-nearest neighbor (B), logistic regression (C), ordiscriminant analysis (D) used to analyze the independent 104 samplevalidation set. Significant differences between class 1 and class 2 K-Mcurves are observed using each modeling method. Algorithm accuracy ishighest for the logistic regression and discriminant analysis models(ROC=0.8969), and the same was true for sensitivity, or accuracy ofpredicting class 2 high risk cases (sensitivity=91%).

FIG. 4 depicts statistics and K-M analysis reflecting the impact ofstage 0 in situ melanomas on training set predictive power. Removingstage 0 cases from the original training and validation cohorts producesa 149 sample training set (A) and independent 104 sample validation set(C). Significant differences are observed in class 1 and class 2 K-Mcurves for both cohorts (p<0.0001), and metastatic risk prediction isaccurate in both the training and validation sets (ROC=0.9177 and0.9014, respectively). Analysis was also performed following inclusionof all stage 0 samples in the training set, producing a training cohortof 164 samples (B) that exhibited comparable accuracy (ROC=0.9052;Accuracy=83%; sensitivity=85%) compared to the 149 sample training set.No dramatic differences are observed when the independent 104 samplevalidation set is trained using the 164 sample cohort (D) compared to(C).

FIG. 5 depicts MFS statistics and accuracy of the genetic signature whenused to predict metastatic risk in superficial spreading (A) or nodular(B) type CM tumors. K-M MFS was 100% for superficial spreading casespredicted to be class 1, and only 5% for cases predicted to be class 2(p<0.0001). The predictive model had an accuracy of 100% for predictingboth classes, and ROC=1.00. Prediction of risk for nodular tumors wasless accurate, with an ROC=0.9323, accuracy of 82%, and sensitivity of81%, but K-M analysis reflects a significant difference between the 81%5-year MFS for Class 1 and the 7% MFS for Class 2 cases (p<0.0001).

FIG. 6 depicts MFS graphs and statistical analysis following a tri-modalprediction for CM cases at low-risk (FIG. 6A, class A), intermediaterisk (FIG. 6B, class B) or high-risk (FIG. 6C, class C) of metastasisaccording to a radial basis machine (RBM) modeling algorithm. 5-yr MFSfor the independent 104 sample validation set is 98% for class Acompared to 79% for class B and 30% for class C (p<0.0001), with a 64%rate in the overall cohort, reflecting clinically valuable models.

FIG. 7 depicts MFS for the 104 sample validation cohort based upon theprobability score generated for each case during predictive modelinganalysis. Subgroups A-J represent 0.1 unit incremental increases in theprobability of metastasis (0=low risk of metastasis, 1=high risk ofmetastasis), as determined by the RBM algorithm. Importantly, increasesin probability score correspond to decreases in MFS rates. For example,all cases in subgroup A have a probability score between 0 and 0.099,and there is 100% 5 year MFS in the cohort. Conversely, each of thecases is subgroup J has a probability score between 0.9 and 1.0, andnone of those cases is metastasis free at the 5 year time point.Statistically significant differences are observed between the subgroups(p<0.0001), and 5-year MFS is shown in the figure legend.

FIG. 8 depicts Kaplan-Meier analysis of metastasis-free survival (FIG.8A-8B), distant metastasis-free survival (FIG. 8C-8D), and overallsurvival (FIG. 8E-8F) for a 104 case cohort that includes CM Stages I-IV(FIG. 8A, 8C and 8E), and for a 78 case cohort that includes only StageI and II cases with evidence of metastasis, or no evidence of metastasisand greater than 5 years of follow-up (FIG. 8B, 8D and 8F).

FIG. 9 depicts Kaplan-Meier analysis of distant metastasis-free survival(DMFS) comparing gene expression profile (GEP) to American JointCommittee on Cancer T-factors. DMFS is grouped by GEP class assignment(A), Breslow thickness of greater or less than 0.75 mm (B), presence orabsence of ulceration (C), or mitotic rate of greater or less than 1/mm²(D). N reflects the number of cases analyzed for each factor, based uponthe availability of clinical data for those cases, and statisticalsignificance.

FIG. 10 depicts the metastasis free (FIG. 10A, MFS) and overall (FIG.10B, OS) survival in a cohort of cutaneous melanoma primary tumorspecimens collected from patients who underwent a SLNB procedure in thecourse of clinical management of the disease. Cases within the group arestratified according to those who had a negative SLN (SLN−; that isremained stage I or II under current definitions) or positive SLN (SLN+;that is were upstaged to stage III under current definitions) outcomefollowing SLNB to determine the risk of metastatic disease. Of note, 70SLN− patients developed metastatic disease and 46 died, resulting in5-year MFS and OS of only 55% and 70%, respectively, compared to 37% and62%, respectively, for patients with a SLN+ outcome.

FIG. 11 depicts the metastasis free (FIG. 11A, MFS) and overall (FIG.11B, OS) survival in a cohort of cutaneous melanoma primary tumorspecimens collected from patients who underwent a SLNB procedure in thecourse of clinical management of their disease. Cases within the groupare stratified according to those who had a Class 1 or Class 2 outcomefollowing 28-gene GEP analysis to determine the risk of metastaticdisease. Of note, only 16 of 76 Class 1 patients developed metastaticdisease and 10 died of their disease. 5-year MFS and OS was 79% and 89%,respectively, for GEP Class 1 patients, compared to 55% and 70%,respectively, for patients with a SLN− outcome. Class 2 cases hadsimilar 5-year MFS and OS (34% and 54%, respectively) compared to SLN+cases (37% and 62%, respectively).

FIG. 12 depicts metastasis free (FIG. 12A, MFS) and overall (FIG. 12B,OS) survival in a cohort of cutaneous melanoma primary tumor specimenscollected from patients who underwent a SLNB procedure in the course ofclinical management of their disease. Cases within the group arestratified according to the combined outcomes predicted using the28-gene GEP in combination with SLNB. Class 1/SLN− cases have the bestprognosis for both MFS (83%) and OS (91%). Of significance, Class 2patients with either a SLN− or SLN+ result have highly similar MFS (35%vs. 33%, respectively) and OS (54% vs. 57%, respectively), indicatingthat the GEP prognosis is more accurate than SLNB prognosis in high riskpatients.

FIG. 13 depicts the comparison of metastasis free (FIG. 13A, MFS) anddistant metastasis free (FIG. 13B, DMFS) survival in a cohort ofcutaneous melanoma primary tumor specimens collected from patients whounderwent a SLNB procedure in the course of clinical management of theirdisease. MFS includes metastasis within the nodal basin (regionalrecurrence and in transit disease), while DMFS includes only metastasisbeyond the nodal basin. Cases within the group are stratified accordingto the combined outcomes predicted using the 28-gene GEP in combinationwith SLNB. Class 1/SLN− cases have the best prognosis for both MFS (83%)and DMFS (86%). The comparison of MFS and DMFS highlight the utility ofGEP for predicting regional recurrence, and also reflect the value ofGEP in combination with SLNB for predicting distant metastasis in CMpatients.

FIG. 14 depicts the surgical and prognostic advantages of including theGEP signature with AJCC T-stage factors and SLNB. Using T-stage alone(A), 217 CM cases were provided with a SLNB procedure. In this cohort,58 cases were had a SLNB+ outcome, reflecting a 27% surgical yield.Addition of the GEP signature to T-factors (B) would have potentiallyreduced the number of SLNB procedures from 217 to 141, removing 76 (35%)cases, and identifying 49 cases with a SLNB+ outcome. Therefore, addingthe GEP signature to AJCC staging provided a surgical yield of 35%(49/141) compared to 27% (58/217) with AJCC staging alone. A prognosticadvantage was also observed for the GEP, as SLNB procedure (C)identified 37/107 cases (sensitivity=35%) with a documented metastaticevent, while adding GEP to SLNB (D) identified 91/107 cases(sensitivity=85%) with a documented metastatic event. Of the 16metastatic cases called Class 1 by the GEP signature, 9 had a SLN+outcome. Of the 9, the GEP signature misclassified only 4 cases (1.8%)that had a distant metastatic event, and 3 cases (1.4%) that died fromtheir disease. Overall, the GEP signature provided a netreclassification improvement of 48% in the 217 sample cohort of CMcases.

DETAILED DESCRIPTION OF THE INVENTION

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the invention belongs. Although methods and materialssimilar or equivalent to those described herein can be used to practicethe invention, suitable methods and materials are described below. Allpublications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including definitions, willcontrol. In addition, the materials, methods, and examples areillustrative only and are not intended to be limiting. Other featuresand advantages of the invention will be apparent from the followingdetailed description. Applicants reserve the right to alternativelyclaim any disclosed invention using the transitional phrase“comprising,” “consisting essentially of,” or “consisting of,” accordingto standard practice in patent law.

Methods well known to those skilled in the art can be used to constructgenetic expression constructs and recombinant cells according to thisinvention. These methods include in vitro recombinant DNA techniques,synthetic techniques, in vivo recombination techniques, and polymerasechain reaction (PCR) techniques. See, for example, techniques asdescribed in Maniatis et al., 1989, MOLECULAR CLONING: A LABORATORYMANUAL, Cold Spring Harbor Laboratory, New York; Ausubel et al., 1989,CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, Greene Publishing Associates andWiley Interscience, New York, and PCR Protocols: A Guide to Methods andApplications (Innis et al., 1990, Academic Press, San Diego, Calif.).

Before describing the present invention in detail, a number of termswill be defined. As used herein, the singular forms “a”, “an”, and “the”include plural referents unless the context clearly dictates otherwise.For example, reference to a “nucleic acid” means one or more nucleicacids.

It is noted that terms like “preferably”, “commonly”, and “typically”are not utilized herein to limit the scope of the claimed invention orto imply that certain features are critical, essential, or evenimportant to the structure or function of the claimed invention. Rather,these terms are merely intended to highlight alternative or additionalfeatures that can or cannot be utilized in a particular embodiment ofthe present invention.

For the purposes of describing and defining the present invention it isnoted that the term “substantially” is utilized herein to represent theinherent degree of uncertainty that can be attributed to anyquantitative comparison, value, measurement, or other representation.The term “substantially” is also utilized herein to represent the degreeby which a quantitative representation can vary from a stated referencewithout resulting in a change in the basic function of the subjectmatter at issue.

As used herein, the terms “polynucleotide”, “nucleotide”,“oligonucleotide”, and “nucleic acid” can be used interchangeably torefer to nucleic acid comprising DNA, RNA, derivatives thereof, orcombinations thereof.

The inventors reviewed the current state of the art for evidence ofmicroarray analyses focused on distinguishing differential geneticexpression profiles that characterize cutaneous melanoma tumors. Sevenstudies were identified that utilized microarray technology to determinegenetic expression comparing various stages of cutaneousmelanoma.^(22-27,29) The objective was to identify genes that weredysregulated between radial growth phase to vertical growth phase, or inprimary melanomas compared to metastatic tumors. Mauerer et al.,assessed gene expression differences in melanocytic nevi compared toprimary melanoma, melanocytic nevi compared to metastatic melanoma, andprimary melanoma compared to metastatic melanoma²⁵. Nothing in the artwas found relating to evaluation of primary cutaneous melanoma tumorsrelative to subsequent metastatic versus non-metastatic outcomes. In anattempt to arrive at a putative gene expression profile set, theinventors focused on genes isolated from primary melanoma tumor samplesand metastatic melanoma tumor samples that had an observed up-regulationgreater than 2-fold, or down-regulation greater than 3-fold, andestablished those as potential mediators of metastatic progression. Bythese criteria, 26 up-regulated genes and 78 down-regulated genes werechosen as the basis for comparison to other expression analysis studies.This 104-gene panel was subsequently compared to expression data setsreported in Scatolini et al., Jaeger, et al., Winnipenninckx et al.,Haqq, et al., Smith et al., and Bittner et al.^(22-24,26,27,29)Additionally, the expression data from Onken, et al., which reported 74genes that were differentially regulated in metastatic andnon-metastatic uveal melanoma tumors was compared to the 104-genepanel.¹⁴

In one embodiment, the invention as disclosed herein is a method forpredicting risk of metastasis, overall survival, or both, in a patientwith a primary cutaneous melanoma tumor, the method comprising: (a)measuring gene expression levels of at least eight genes selected fromthe group consisting of BAP1_varA, BAP1_varB, MGP, SPP1, CXCL14, CLCA2,S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B, S100A9, CRABP2,KRT14, ROBOT, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL,LTA4H, and CST6, in a sample of the primary cutaneous melanoma tumor,wherein measuring gene expression levels of the at least eight genescomprises measurement of a level of fluorescence by a sequence detectionsystem following RT-PCR of the at least eight genes; (b) determining apatient gene-expression profile signature comprising the gene expressionlevels of the at least eight genes; (c) comparing the patientgene-expression profile signature to a gene-expression profile of apredictive training set; and (d) providing an indication as to a risk ofmetastasis, overall survival or both for the primary cutaneous melanomatumor when the patient gene expression profile indicates that theexpression levels of at the least eight genes are altered in apredictive manner as compared to the gene expression profile of thepredictive training set.

In an embodiment, the risk of metastasis for the primary cutaneousmelanoma tumor is classified from a low risk of metastasis to a highrisk of metastasis (for example, the tumor has a graduated risk from lowrisk to high or high to low risk of metastasis). In other embodiments,low risk of metastasis refers to 5-yr metastasis free survival rates ofgreater than 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or more,and high risk of metastasis refers to a 5-yr metastasis free survivalrates of less than 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5% orless. In yet another embodiment, class 1 indicates that the tumor is ata low risk of metastasis, class 2 indicates that the tumor is at a highrisk of metastasis, class A indicates that the tumor is at a low risk ofmetastasis, class B indicates that the tumor is at an intermediate riskof metastasis and class C indicates that the tumor is at a high risk ofmetastasis.

In some embodiments, the methods described herein can comprisedetermining that the primary cutaneous melanoma tumor has an increasedrisk of metastasis or decreased overall survival by combining with TNM(Tumor-Node-Metastasis) status, clinical staging set by American JointCommittee on Cancer (AJCC) to stage the primary cutaneous melanomatumor, or by combining with sentinel lymph node biopsy status, or allthree. In other embodiments, (1) Breslow thickness of greater or lessthan 0.75 mm; or (2) presence or absence of ulceration; or (3) mitoticrate of greater or less than 1/mm²; or (4) sentinel lymph node biopsystatus or any combination of the four can be used in combination withthe gene-expression signature of a primary cutaneous melanoma. In anembodiment, a sentinel lymph node biopsy was performed in the patientfrom which the primary cutaneous melanoma tumor was separately obtained.In another embodiment the sentinel lymph node biopsy was negative.

In another embodiment, the invention as disclosed herein is a method fora method of treating cutaneous melanoma in a patient, the methodcomprising: (a) measuring gene expression levels of at least eight genesselected from the group consisting of BAP1_varA, BAP1_varB, MGP, SPP1,CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B,S100A9, CRABP2, KRT14, ROBOT, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29,AQP3, TYRP1, PPL, LTA4H, and CST6 in a sample of a primary cutaneousmelanoma tumor in the patient, wherein measuring gene expression levelsof the at least eight genes comprises determining a level offluorescence by a sequence detection system following RT-PCR of the atleast eight genes; (b) determining a patient gene-expression profilesignature comprising the gene expression levels of the at least eightgenes; (c) comparing the patient gene-expression profile signature to agene-expression profile of a predictive training set; (d) making adetermination as to whether the patient gene-expression profilesignature of the at least eight genes is altered in a predictive manner;and (e) administering an aggressive cancer treatment regimen to thepatient when the determination is made in the affirmative that thepatient has a primary cutaneous melanoma tumor with an increased risk ofmetastasis or decreased overall survival.

In an additional embodiment, the invention as disclosed herein is amethod of treating cutaneous melanoma in a patient, the methodcomprising: (a) measuring gene expression levels of at least eight genesselected from the group consisting of BAP1_varA, BAP1_varB, MGP, SPP1,CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B,S100A9, CRABP2, KRT14, ROBOT, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29,AQP3, TYRP1, PPL, LTA4H, and CST6 in a sample of a primary cutaneousmelanoma tumor in the patient, wherein measuring gene expression levelsof the at least eight genes comprises determining a level offluorescence by a sequence detection system following RT-PCR of the atleast eight genes; (b) determining a patient gene-expression profilesignature comprising the gene expression levels of the at least eightgenes; (c) comparing the patient gene-expression profile signature to agene-expression profile of a predictive training set; (d) making adetermination as to whether the patient gene-expression profilesignature of the at least eight genes is altered in a predictive manner;and (e) performing a sentinel lymph node biopsy (SLNB) on the patientwhen the determination is made in the affirmative that the patient has aprimary cutaneous melanoma tumor with an increased risk of metastasis ordecreased overall survival.

As used here in, “primary cutaneous melanoma tumor” refers to anyprimary melanoma lesion, regardless of tumor thickness, in patientswithout clinical or histologic evidence of regional or distantmetastatic disease and which may be obtained through a variety ofsampling methods such as punch biopsy, shave biopsy, wide localexcision, and other means of extracting RNA from the primary melanomalesion.

As used here in, “metastasis” is defined as the recurrence or diseaseprogression that may occur locally (such as local recurrence and intransit disease), regionally (such as nodal micrometastasis ormacrometastasis), or distally (such as brain, lung and other tissues).“Class 1 or class 2 of metastasis” as defined herein includes low-risk(class 1) or high-risk (class 2) of metastasis according to any of thestatistical methods disclosed herein. Additionally, “cutaneous melanomametastasis” as used herein includes sentinel lymph node metastasis, intransit metastasis, distant metastasis, and local recurrence.

As used herein, “overall survival” (OS) refers to the percentage ofpeople in a study or treatment group who are still alive for a certainperiod of time after they were diagnosed with or started treatment for adisease, such as cancer. The overall survival rate is often stated as afive-year survival rate, which is the percentage of people in a study ortreatment group who are alive five years after their diagnosis or thestart of treatment.

The phrase “measuring the gene-expression levels” as used herein refersto determining or quantifying RNA or proteins expressed by the gene orgenes. The term “RNA” includes mRNA transcripts, and/or specific splicedvariants of mRNA. The term “RNA product of the gene” as used hereinrefers to RNA transcripts transcribed from the gene and/or specificspliced variants. In the case of “protein”, it refers to proteinstranslated from the RNA transcripts transcribed from the gene. The term“protein product of the gene” refers to proteins translated from RNAproducts of the gene. A number of methods can be used to detect orquantify the level of RNA products of the gene or genes within a sample,including microarrays, RT-PCR (including quantitative RT-PCR), nucleaseprotection assays and Northern blot analyses. In one embodiment, theassay uses the APPLIED BIOSYSTEMS™ HT7900 fast Real-Time PCR system. Inaddition, a person skilled in the art will appreciate that a number ofmethods can be used to determine the amount of a protein product of agene of the invention, including immunoassays such as Western blots,ELISA, and immunoprecipitation followed by SDS-PAGE andimmunocytochemistry.

A person skilled in the art will appreciate that a number of detectionagents can be used to determine the expression of the genes. Forexample, to detect RNA products of the biomarkers, probes, primers,complementary nucleotide sequences or nucleotide sequences thathybridize to the RNA products can be used. To detect protein products ofthe biomarkers, ligands or antibodies that specifically bind to theprotein products can be used.

The term “hybridize” refers to the sequence specific non-covalentbinding interaction with a complementary nucleic acid. In an embodiment,the hybridization is under high stringency conditions. Appropriatestringency conditions which promote hybridization are known to thoseskilled in the art.

The term “probe” as used herein refers to a nucleic acid sequence thatwill hybridize to a nucleic acid target sequence. In one example, theprobe hybridizes to an RNA product of the gene or a nucleic acidsequence complementary thereof. The length of probe depends on thehybridizing conditions and the sequences of the probe and nucleic acidtarget sequence. In one embodiment, the probe is at least 8, 10, 15, 20,25, 50, 75, 100, 150, 200, 250, 400, 500 or more nucleotides in length.

As used herein, a “sequence detection system” is any computationalmethod in the art that can be used to analyze the results of a PCRreaction. One example, inter alia, is the APPLIED BIOSYSTEMS™ HT7900fast Real-Time PCR system. In certain embodiments, gene expression canbe analyzed using, e.g., direct DNA expression in microarray, Sangersequencing analysis, Northern blot, the NANOSTRING® technology, serialanalysis of gene expression (SAGE), RNA-seq, tissue microarray, orprotein expression with immunohistochemistry or western blot technique.

As used herein the term “differentially expressed” or “differentialexpression” refers to a difference in the level of expression of thegenes that can be assayed by measuring the level of expression of theproducts of the genes, such as the difference in level of messenger RNAtranscript expressed or proteins expressed of the genes. In anembodiment, the difference is statistically significant. The term“difference in the level of expression” refers to an increase ordecrease in the measurable expression level of a given gene as measuredby the amount of messenger RNA transcript and/or the amount of proteinin a sample as compared with the measurable expression level of a givengene in a control. In another embodiment, the differential expressioncan be compared using the ratio of the level of expression of a givengene or genes as compared with the expression level of the given gene orgenes of a control, wherein the ratio is not equal to 1.0. For example,an RNA or protein is differentially expressed if the ratio of the levelof expression in a first sample as compared with a second sample isgreater than or less than 1.0. For example, a ratio of greater than 1,1.2, 1.5, 1.7, 2, 3, 3, 5, 10, 15, 20 or more, or a ratio less than 1,0.8, 0.6, 0.4, 0.2, 0.1, 0.05, 0.001 or less. In yet another embodimentthe differential expression is measured using p-value. For instance,when using p-value, a biomarker is identified as being differentiallyexpressed as between a first sample and a second sample when the p-valueis less than 0.1, preferably less than 0.05, more preferably less than0.01, even more preferably less than 0.005, the most preferably lessthan 0.001.

As used herein, the terms “control” and “standard” refer to a specificvalue that one can use to determine the value obtained from the sample.In one embodiment, a dataset may be obtained from samples from a groupof subjects known to have a cutaneous melanoma type or subtype. Theexpression data of the genes in the dataset can be used to create acontrol (standard) value that is used in testing samples from newsubjects. In such an embodiment, the “control” or “standard” is apredetermined value for each gene or set of genes obtained from subjectswith cutaneous melanoma subjects whose gene expression values and tumortypes are known.

As defined herein, “gene-expression profile signature” is anycombination of genes, the measured messenger RNA transcript expressionlevels or direct DNA expression levels or immunohistochemistry levels ofwhich can be used to distinguish between two biologically differentcorporal tissues and/or cells and/or cellular changes. In certainembodiments, gene-expression profile signature is comprised of thegene-expression levels of at least 28, 27, 26, 25, 24, 23, 22, 21, 20,19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, or 8 genes. Ina furtherembodiments, the genes selected are: (a) KRT6B, GJA1, AQP3, TRIM29,TYRP1, RBM23, MGP and EIF1B; (b) SAP130, ARG1, KRT6B, EIF1B, S10A9,KRT14, ROBOT, RBM23, TRIM29, AQP3, TYRP1 and CST6; (c) GJA1, PPL, ROBOT,MGP, TRIM29, AQP3, RBM23, TACSTD2, TYRP1, KRT6B, EIF1B and DSC1; (d)CRABP2, TYRP1, PPL, EIF1B, SPRR1B, DSC1, GJA1, AQP3, MGP, RBM23, CLCA2and TRIM29; (e) RBM23, TACSTD2, CRABP2, PPL, GJA1, SPP1, CXCL14, EIF1B,AQP3, MGP, LTA4H and KRT6B; (f) S100A8, TACSTD2, BAP1_varA, KRT6B,EIF1B, TRIM29, TYRP1, CST6, PPL, RBM23, AQP3, GJA1, SPRR1B and ARG1; (g)CST6, KRT6B, LTA4H, CLCA2, CRABP2, TRIM29, CXCL14, PPL, ARG1, RBM23,GJA1, AQP3, TYRP1, SPP1, DSC1, TACSTD2, EIF1B, and BAP1_varA.

As defined herein, “predictive training set” means a cohort of CM tumorswith known clinical metastatic outcome and known genetic expressionprofile, used to define/establish all other CM tumors, based upon thegenetic expression profile of each, as a low-risk, class 1 tumor type ora high-risk, class 2 tumor type. Additionally, included in thepredictive training set is the definition of “threshold points” pointsat which a classification of metastatic risk is determined, specific toeach individual gene expression level.

As defined herein, “altered in a predictive manner” means changes ingenetic expression profile that predict metastatic risk or predictoverall survival. Predictive modeling risk assessment can be measuredas: 1) a binary outcome having risk of metastasis or overall survivalthat is classified as low risk (e.g., termed Class 1 herein) vs. highrisk (e.g., termed Class 2 herein); and/or 2) a linear outcome basedupon a probability score from 0 to 1 that reflects the correlation ofthe genetic expression profile of a cutaneous melanoma tumor with thegenetic expression profile of the samples that comprise the training setused to predict risk outcome. Within the probability score range from 0to 1, a probability score, for example, less than 0.5 reflects a tumorsample with a low risk of metastasis or death from disease, while aprobability score, for example, greater than 0.5 reflects a tumor samplewith a high risk of metastasis or death from disease. The increasingprobability score from 0 to 1 reflects incrementally decliningmetastasis free survival, as illustrated for example in FIG. 7. Forexample, within subsets A, B and C in FIG. 7, cutaneous melanoma tumorsthat have a probability score from 0 to 0.299 exhibit 5-year metastasisfree survival rates are 100%, and rates remain above 90% for subsets D(0.3-0.399) and E (0.4-0.499). Conversely, tumor subsets F (0.5-0.599,50% 5-yr MFS), G (0.6-0.699, 45% 5-yr MFS), H (0.7-0.799, 33% 5-yr MFS),I (0.8-0.899, 25% 5-yr MFS) and J (0.9-1.0, 10% 5-yr MFS), withprobability scores between 0.5 and 1 exhibit significant decreases in5-year survival rates with each 0.1 incremental increase in theprobability score.

To develop a ternary, or three-class system of risk assessment, withClass A having a low risk of metastasis or death from disease, Class Bhaving an intermediate risk, and Class C having a high risk, the medianprobability score value for all low risk or high risk tumor samples inthe training set was determined, and one standard deviation from themedian was established as a numerical boundary to define low or highrisk. For example, as shown in FIG. 6, low risk (Class A) cutaneousmelanoma tumors within the ternary classification system have a 5-yearmetastasis free survival of 98%, compared to high risk (Class C) tumorswith a 30% 5-year rate. Cases falling outside of one standard deviationfrom the median low or high risk probability scores have an intermediaterisk, and intermediate risk (Class B) tumors have a 79% 5-yearmetastasis free survival rate.

The TNM (Tumor-Node-Metastasis) status system is the most widely usedcancer staging system among clinicians and is maintained by the AmericanJoint Committee on Cancer (AJCC) and the International Union for CancerControl (UICC). Cancer staging systems codify the extent of cancer toprovide clinicians and patients with the means to quantify prognosis forindividual patients and to compare groups of patients in clinical trialsand who receive standard care around the world. Clinical stagingincludes microstaging of the primary melanoma and clinical/radiologicevaluation for metastases. By convention, clinical staging should beused after complete excision of the primary melanoma with clinicalassessment for regional and distant metastasis. The AJCC and the UICCupdate the TNM status cancer staging system periodically. The mostrecent revision is the 7th edition, effective for cancers diagnosed onor after Jan. 1, 2010 (Edge S B, Byrd D R, Compton C C, Fritz A G,Greene F L, Trotti A, editors. AJCC cancer staging manual (7th ed). NewYork, N.Y.: Springer; 2010, pages 325-344). Tumor-classification issummarized below in Table A.

TABLE A T-classification of melanoma of the skin THICKNESS ULCERATIONT-CLASSIFICATION (mm) STATUS/MITOSES T1 ≦1.0 a: w/o ulceration andmitosis <1/mm² b: with ulceration or mitoses ≧1/mm² T2 1.01-2.0 a: w/oulceration b: with ulceration T3 2.01-4.0 a: w/o ulceration b: withulceration T4 >4.0 a: w/o ulceration b: with ulceration

As defined herein, “sentinel lymph node biopsy” refers is a procedure inwhich the first lymph node(s) (i.e., the sentinel lymph node) to whichcancer cells are most likely to spread from a primary tumor areidentified, removed, and examined to determine whether cancer cells arepresent. A sentinel lymph node biopsy (SLNB) can be used to helpdetermine the extent, or stage, of cancer in the body. During the SLNBprocedure, multiple SLN's are biopsied. A negative SLNB result cansuggest that cancer has not developed the ability to spread to nearbylymph nodes or other organs. A positive SLNB result indicates thatcancer is present in the sentinel lymph node and may be present in othernearby lymph nodes (called regional lymph nodes) and, possibly, otherorgans. As a surgical intervention, SLNB has a low yield with only 5% ofpatients with CM staged at 1b that undergo SLNB yielding a positiveSLNB, and only 18% of stage II patients yielding a positive SLNB. SNLBcan have adverse effects. The potential adverse effects of lymph nodesurgery include the following: risk of complications from generalanesthesia, lymphedema, or tissue swelling; seroma, or the buildup oflymph fluid at the site of the surgery; numbness, tingling, low of motorfunction or pain at the site of the surgery; difficulty moving theaffected body part; and infection. SLNB, like other surgical procedures,can cause short-term pain, swelling, and bruising at the surgical siteand increase the risk of infection. In addition, some patients may haveskin or allergic reactions to the blue dye used in SLNB. Anotherclinically significant harm is a false-negative biopsy result—that is,cancer cells are not seen in the sentinel lymph node although they maybe present in the lymphatic system—perhaps they haven't reached theSLNB, were biologically cleared from the SLNB and may be presentregional lymph nodes, spread hematogenously, or other parts of the body.A false-negative biopsy result under-stages the patient, resulting inunder-treatment of the patient's true risk of developing metastaticdisease and gives the patient and the doctor a false sense of securityabout the extent of cancer in the patient's body. As demonstrated by theMSLT-1 study and the AJCC guidelines, approximately twice as manypatients who are SLNB negative will metastasize than those that are SLNBpositive.

As defined herein, “aggressive cancer treatment regimen” is determinedby a medical professional and can be specific to each patient. Anaggressive cancer treatment regimen is defined by the NationalComprehensive Cancer Network (NCCN), and has been defined in the NCCNGuidelines® as including one or more of 1) intensified imaging (CT scan,PET/CT, MRI, chest X-ray), 2) discussion and/or offering of sentinellymph node biopsy with subsequent partial or complete lymphadenectomy,3) inclusion in ongoing clinical trials, and 4) therapeutic interventionwith interferon alpha treatment and radiation to nodal basin. Guidelinesfor clinical practice are published in the National Comprehensive CancerNetwork (NCCN Guidelines® Melanoma version 2.2013 available on the WorldWide Web at NCCN.org). Additional therapeutic options include, but arenot limited to, injections of Bacille Calmette-Guerin (BCG) vaccine,interferon, or interleukin-2 (IL-2) directly into the melanoma;radiation therapy without chemotherapeutic intervention; or applyingimiquimod (ALDARA®) cream. For melanomas on an arm or leg, anotheroption might be isolated limb perfusion (i.e., infusing the limb with aheated solution of chemotherapy). Other possible treatments includetargeted therapy, such as immunotherapy (e.g., ipilimumab/YERVOY®),chemotherapy (e.g., dacarbazine/DTIC® and temozolomide/TEMODAR®), orimmunotherapy combined with chemotherapy (i.e., bio chemotherapy).

As used herein, the terms treatment, treat, or treating refers to amethod of reducing the effects of a disease or condition or symptom ofthe disease or condition. Thus, in the disclosed method, treatment canrefer to a 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%reduction in the severity of an established disease or condition orsymptom of the disease or condition. For example, a method of treating adisease is considered to be a treatment if there is a 5% reduction inone or more symptoms of the disease in a subject as compared to acontrol. Thus, the reduction can be a 5%, 10%, 20%, 30%, 40%, 50%, 60%,70%, 80%, 90%, 100% or any percent reduction between 5 and 100% ascompared to native or control levels. It is understood that treatmentdoes not necessarily refer to a cure or complete ablation of thedisease, condition, or symptoms of the disease or condition.

In one embodiment, the cutaneous melanoma tumor is taken from aformalin-fixed, paraffin embedded wide local excision sample. In anotherembodiment, the cutaneous melanoma tumor is taken from formalin-fixed,paraffin embedded punch biopsy sample. In another embodiment, thecutaneous melanoma tumor is taken from formalin-fixed, paraffin embeddedshave biopsy sample.

In certain embodiments, analysis of genetic expression and determinationof outcome is carried out using radial basis machine and/or partitiontree analysis, logistic regression analysis (LRA), K-nearest neighbor,or other algorithmic approach. These analysis techniques take intoaccount the large number of samples required to generate a training setthat will enable accurate prediction of outcomes as a result ofcut-points established with an in-process training set or cut-pointsdefined for non-algorithmic analysis, but that any number of linear andnonlinear approaches can produce a statistically significant andclinically significant result. Among the advantages of use of themethods disclosed herein are relating to, e.g., the in excess of 140samples in the training set used to cover either heterogeneity oradequately handle smaller gene expression profile changes that could notadequately predict outcomes in an independent test set. As definedherein, “Kaplan-Meier survival analysis” is understood in the art to bealso known as the product limit estimator, which is used to estimate thesurvival function from lifetime data. In medical research, it is oftenused to measure the fraction of patients living for a certain amount oftime after treatment. JMP GENOMICS® software provides an interface forutilizing each of the predictive modeling methods disclosed herein, andshould not limit the claims to methods performed only with JMP GENOMICS®software.

EXAMPLES

The Examples that follow are illustrative of specific embodiments of theinvention, and various uses thereof. They are set forth for explanatorypurposes only, and should not be construed as limiting the scope of theinvention in any way.

Materials and Methods 1. Cutaneous Melanoma Tumor Sample Preparation andRNA Isolation

Formalin fixed paraffin embedded (FFPE) primary cutaneous melanoma tumorspecimens arranged in 5 μm sections on microscope slides were acquiredfrom multiple institutions under Institutional Review Board (IRB)approved protocols. All tissue was reviewed by a pathologist to confirmthe presence of melanoma and the dissectible tumor area was marked.Tumor tissue was dissected from the slide using a sterile disposablescalpel, collected into a microcentrifuge tube, and deparaffinized usingxylene. RNA was isolated from each specimen using the Ambion RECOVERALL™Total Nucleic Acid Isolation Kit (Life Technologies Corporation, GrandIsland, N.Y.). RNA quantity and quality were assessed using theNANODROP™ 1000 system and the Agilent Bioanalyzer 2100.

2. cDNA Generation and RT-PCR Analysis

RNA isolated from FFPE samples was converted to cDNA using the APPLIEDBIOSYSTEMS™ High Capacity cDNA Reverse Transcription Kit (LifeTechnologies Corporation, Grand Island, N.Y.). Prior to performing theRT-PCR assay each cDNA sample underwent a 14-cycle pre-amplificationstep. Pre-amplified cDNA samples were diluted 20-fold in TE buffer. 50ul of each diluted sample was mixed with 50 ul of 2× TAQMAN® GeneExpression Master Mix, and the solution was loaded to a custom highthroughput microfluidics gene card containing primers specific for 28class discriminating genes and 3 endogenous control genes (HNRNPL, YKT2and FXR1). Each sample was run in triplicate. The gene expressionprofile test was performed on an APPLIED BIOSYSTEMS™ HT7900 machine(Life Technologies Corporation, Grand Island, N.Y.).

3. Expression Analysis and Class Assignment

Mean C_(t) values were calculated for triplicate sample sets, and ΔC_(t)values were calculated by subtracting the mean C_(t) of eachdiscriminating gene from the geometric mean of the mean C_(t) values ofall three endogenous control genes (HNRNPL, YKT2 and FXR1). ΔC_(t)values were standardized according to the mean of the expression of alldiscriminant genes with a scale equivalent to the standard deviation.Three control genes were selected based upon analysis using geNorm.Various linear and non-linear predictive modeling methods, includingradial basis machine, k-nearest neighbor, partition tree, logisticregression, discriminant analysis and distance scoring, were performedusing JMP GENOMICS® SAS-based software (JMP, Cary, N.C.). Kaplan-Meiercurves reflecting metastasis free survival were also generated in JMP,and statistical significance was calculated according to the Log Rankmethod. Cox univariate and multivariate regression analysis wasperformed using WinSTAT for Microsoft Excel version 2012.1.

Example 1 Cutaneous Melanoma Metastatic Risk Genetic Signature andBiomarker Expression

Genetic expression of the discriminant genes in the signature (Table 1)was assessed in a cohort of 268 cutaneous melanoma samples using RT-PCR(FIG. 1). As shown in Table 2 below, of the 28 discriminating genes, 26were significantly altered in metastatic melanoma tumors compared tononmetastatic tumors (p<0.05, range 0.0366-6.08E-16), and 25 weredown-regulated. Genes that were up-regulated in the metastatic tumorsincluded SPP1, KRT6B, and EIF1B.

TABLE 1 Genes included in the GEP signature able to predict metastaticrisk from primary CM tumors. Gene Symbol Gene Name Alternative GeneNames BAP1_varA BRCA1 associated protein-1 TPDS, UCHL2, HUCEP-13,HUCEP-6, BAP_var1, BAP (a1) BAP1_varB BRCA1 associated protein-1 UCHL2,HUCEP-13, HUCEP-6, BAP_var2, BAP (a2) MGP matrix gamma-carboxyglutamicmatrix Gla protein, gamma-carboxyglutamic acid acid, GIG36, MGLAP, NTISPP1 secreted phosphoprotein 1 BNSP, BSPI, ETA-1, OPN, PSEC0156 CXCL14chemokine (C—X—C motif) ligand 14 small inducible cytokine subfamily Bmember 14, UNQ240/PRO273, BMAC, BRAK, KEC, KS1, MIP-2g, MIP2G, NJAC,SCYB14 CLCA2 chloride channel accessory 2 chloride channel regulator 2,chloride channel calcium activated 2, CACC, CACC3, CLCRG2, CaCC-3 S100A8S100 calcium binding protein A8 calgranulin-A, calprotectin L1L subunit,cystic fibrosis antigen, leukocyte L1 complex light chain, migrationinhibitory factor-related protein 8, urinary stone protein band A,protein S100-A8, 60B8AG, CAGA, CFAG, CGLA, CP-10, L1Ag, MA387, MIF,MRP8, NIF, P8 S100A9 S100 calcium binding protein A9 calgranulin-B,calprotectin L1H subunit, leukocyte L1 complex heavy chain, migrationinhibitory factor-related protein 14, 60B8AG, CAGB, CFAG, CGLB, L1AG,LIAG, MAC387, MIF, MRP14, NIF, P14 BTG1 B-cell translocation gene 1,anti- B-cell translocation gene 1 protein proliferative SAP130Sin3A-associated protein, 130 kDa ARG1 arginase-1 liver-type arginase,type I arginase KRT6B keratin 6B keratin type II cytoskeletal 6B,cytokeratin- 6B, type II keratin Kb10, CK-6B, CK6B, K6B, KRTL1, PC2 GJA1gap junction protein, alpha 1 connexin-43, gap junction 43 kDa heartprotein, AU042049, AW546267, Cnx43, Cx43, Cx43alpha1, Gja-1, Npm1 ID2inhibitor of DNA binding 2, class B basic helix-loop-helix protein,GIG8, dominant negative helix-loop-helix ID2A, ID2H, Idb2, bHLHb26protein EIF1B eukaryotic translation initiation protein translationfactor SUI1 homolog factor 1B GC20 CRABP2 cellular retinoic acid bindingprotein 2 RP11-66D17.5, CRABP-II, RBP6 KRT14 keratin 14 keratin type Icytoskeletal 14, cytokeratin-14, CK14, EBS3, EBS4, K14, NFJ ROBO1roundabout, axon guidance receptor, roundabout homolog 1, deleted in Utwenty homolog 1 (Drosophila) twenty, H-Robo-1, DUTT1, SAX3 RBM23 RNAbinding motif protein 23 RNA-binding region-containing protein 4,splicing factor SF2, PP239, CAPERbeta, RNPC4 TACSTD2 tumor-associatedcalcium signal cell surface glycoprotein Trop-2, membrane transducer 2component chromosome 1 surface marker 1, pancreatic carcinoma markerprotein GA733- 1, GA733-1, TROP2, EGP-1, EGP1, GA7331, GP50, M1S1 DSC1desmocollin 1 cadherin family member 1, desmosomal glycoprotein 2/3,CDHF1, DG2/DG3 SPRR1B small proline-rich protein 1B CORNIFIN,cornifin-B, 14.9 kDa pancornulin, GADD33, SPRR1 TRIM29 tripartite motifcontaining 29 ATDC AQP3 aquaporin 3 aquaglyceroporin-3, AQP-3, GIL TYRP1tyrosinase-related protein 1 RP11-3L8.1, CAS2, CATB, GP75, OCA3, TRP,TRP1, TYRP, b-PROTEIN PPL periplakin 190 kDa paraneoplastic pemphigusantigen, 195 kDa cornified envelope precursor protein LTA4H leukotrieneA4 hydrolase LTA-4 hydrolase CST6 cystatin E/M

TABLE 2 Genes included in the GEP signature able to predict metastaticrisk from primary CM tumors. Delta-Ct value Gene nonmetastaticmetastatic expression change in direction of symbol samples samplesmetastatic samples p-value expression change BAP1_varA −1.290 −1.677−0.388 0.007118 down MGP −1.996 −2.190 −0.194 0.48585 down SPP1 −1.0112.224 3.235 6.08E−16 up CXCL14 3.021 0.828 −2.193 3.31E−12 downBAP1_varB 0.381 0.003 −0.378 0.004646 down CLCA2 −3.468 −5.603 −2.1351.02E−08 down S100A8 −0.450 −1.179 −0.728 0.030655 down BTG1 −2.422−3.008 −0.586 0.023606 down SAP130 −1.075 −1.405 −0.329 0.023626 downARG1 −1.645 −4.393 −2.749 1.05E−08 down KRT6B −1.809 −1.222 0.5860.160458 up GJA1 −2.882 −3.652 −0.770 0.034149 down ID2 −0.649 −1.411−0.762 3.91E−06 down EIF1B 0.041 0.350 0.309 0.023747 up S100A9 3.3742.527 −0.847 0.012385 down CRABP2 −0.087 −0.953 −0.866 0.00059  downKRT14 5.654 3.927 −1.727 1.75E−05 down ROBO1 0.100 −0.364 −0.4640.000406 down RBM23 −2.788 −3.161 −0.374 0.018025 down TACSTD2 −3.485−3.984 −0.499 0.03658  down DSC1 −0.102 −2.963 −2.861   7E−09 downSPRR1B 4.622 3.139 −1.482 0.001392 down TRIM29 0.228 −2.239 −2.4672.34E−09 down AQP3 3.413 1.848 −1.565 5.08E−06 down TYRP1 1.276 −0.850−2.125 2.41E−06 down PPL −0.082 −2.233 −2.150 5.59E−11 down LTA4H −0.736−1.275 −0.539 0.000156 down CST6 −0.535 −3.099 −2.563 1.02E−08 down

Example 2 Initial Training Set Development Studies and Comparison toValidation Cohort

Using JMP GENOMICS® software and clinical data analysis, a training setof 164 cutaneous melanoma samples was generated that could accuratelypredict the risk of metastasis based upon the 28 gene signature. Thetraining set contained 15 stage 0 in situ melanomas, 61 stage I, 70stage II, 17 stage III, and 1 stage IV melanomas. Metastatic risk wasassessed using a radial basis machine predictive modeling algorithm,which reports class 1 (low risk of metastasis) or class 2 (high risk ofmetastasis). ΔCt values generated from RT-PCR analysis of the trainingset cohort were standardized to the mean for each gene, with a scaleequivalent to the standard deviation. Analyses were also performed usingKNN, PTA, and discriminant analysis to confirm the results from the RBMapproach (as discussed below). The training set prediction algorithm wasthen validated using an independent cohort of 104 cutaneous melanomasamples, comprised of 57 stage I, 34 stage II, 11 stage III, and 2 stageIV melanomas. Samples in the validation set were standardized prior toanalysis using the factorials generated during standardization of thetraining set. Area under the receiver operator characteristic (ROC)curve, accuracy, sensitivity (prediction of high risk metastatic event),and specificity (prediction of nonmetastatic outcome) were statisticalendpoints for the analysis. In the training set cohort, ROC=0.9052,accuracy=83%, sensitivity=85%, and specificity=80% (FIG. 1A). In thevalidation cohort, ROC=0.9089, accuracy=86%, sensitivity=89%, andspecificity=83% (FIG. 1B).

Kaplan-Meier survival analysis was also performed for the training andvalidation cohorts. In the training set, 5-year metastasis free survival(MFS) was 91% for class 1 cases, and 25% for class 2 cases (p<0.0001;FIG. 1A). By comparison, 5-year MFS for the validation cohort was 97%for class 1 cases and 31% for class 2 cases (p<0.0001; FIG. 1B).Overall, the 5-year MFS rate for the entire training set, combiningclass 1 and class 2 cases, was 64%, while the combined MFS rate for thevalidation set was 69%.

Example 3 Analysis of 162 Sample Training Set with Multiple PredictiveModeling Methods

JMP GENOMICS® software allows for analysis using linear and non-linearpredictive modeling methods. To assess whether accuracy of metastaticrisk prediction for the validation cohort was limited to the RBM method,partition tree, K-nearest neighbor, logistic regression, anddiscriminant analysis were performed (FIGS. 2 and 3). Training set ROC,accuracy, sensitivity, specificity and K-M 5-year MFS for class 1 andclass 2 cases were highly comparable to the RBM method. Highly accurateprediction of metastasis, and significantly different 5-year MFS betweenclass 1 and class 2 cases was observed when using partition tree (FIG.2A), K-nearest neighbor (FIG. 2B), logistic regression (FIG. 2C), ordiscriminant analysis (FIG. 2D). Significant differences in 5-year MFSwere also observed for validation cohort class 1 and class 2 cases usingpartition tree (FIG. 3A), K-nearest neighbor (FIG. 3B), logisticregression (FIG. 3C), and discriminant analysis (FIG. 3D). Importantly,though, accuracy of prediction for validation samples was above 80% withall methods except partition tree analysis (FIG. 3A), and sensitivity,or accuracy of prediction for cases with documented metastatic events,was as high as 91% when using logistic regression or discriminantanalysis.

Example 4 Evaluation of In Situ Melanoma Effect Upon Training SetAccuracy

To assess the impact of stage 0 in situ melanoma samples on thepredictive capabilities of the training set, either a) samples wereremoved from all stage 0 the training and validation cohorts, generatinga new training set of 149 cases; or b) all stage 0 samples were includedin the training set only, generating a training set comprised of 164cases. The new cutaneous melanoma predictive training sets were used totrain a 104 sample validation cohort.

Radial basis machine prediction was performed as described above, andROC, accuracy of prediction, and 5-year MFS were assessed for thetraining and validation sets (FIG. 4). Training set statistics for boththe 149 sample and 164 sample training sets (FIGS. 4A and 4B) werehighly comparable. ROC was highest for the 149 sample set, but accuracy,sensitivity and specificity were highly similar in the 149 and 164sample training sets. K-M analysis yielded highly significantdifferences between class 1 and class 2 MFS with each training set.Metastatic risk of the 104 samples in the validation cohort wasaccurately predicted with the 149 and 164 sample training sets (FIGS. 4Cand 4D). Both validation sets had ROC greater than 0.9, reflectinghighly relevant clinical models of prediction, and sensitivity of 83%and 89% when class prediction was done using the 149 and 164 sampletraining sets, respectively. Again, MFS was significantly differentbetween class 1 and class 2 regardless of the training set cohort usedto predict risk in the validation set.

Example 5 Identification of Reduced Discriminant Gene Signatures withPredictive Accuracy

Accurate prediction of high risk metastatic cases is extremely importantin order to prevent those patients who are likely to metastasize fromreceiving a low risk treatment protocol. Thus, a measure of success forthe predictive gene set is achievement of greater than 88% sensitivityin both the training and validation sets. As shown in Table 3 below, andin FIG. 4, sensitivity is 85% for the 164 sample training set and 89%for the 104 sample validation set when the 28-gene signatureincorporating all discriminant genes is used to predict metastatic risk.Smaller sets of genes were generated that had equal or increasedsensitivity compared to the 28-gene signature (Table 3, bolded).However, the majority of gene sets that did not include all 28 geneswere not able to produce the sensitivity thresholds required for use ina clinically feasible GEP test.

TABLE 3 Sensitivity, or accuracy of predicting a metastatic event,achieved when using the 28-gene signature or smaller subsets of genes.Sensitivity # of training/validation Gene set variables sets (%) Alldiscriminant genes 28 89/94 SPP1, CXCL14, BAP1_varB, CLCA2, S100A8, BTG16 79/89 TACSTD2, RBM23, PPL, S100A8, MGP, TYRP1 6 86/78 SAP130, ARG1,KRT6B, GJA1, EIF1B, ID2 6 81/81 CRABP2, KRT14, ROBO1, RBM23, TACSTD2,DSC1 6 70/81 ROBO1, CST6, BAP1_varB, ID2, SPRR1B, KRT6B 6 75/83 SPRR1B,AQP3, PPL, DSC1, TYRP1, TRIM29 6 81/81 KRT6B, GJA1, AQP3, TRIM29, TYRP1,RBM23, MGP, 8 93/94 EIF1B SPP1, MGP, KRT6B, PPL, RBM23, AQP3, CXCL14,GJA1 8 86/92 BAP1_varB, S100A8, ARG1, S100A9, RBM23, DSC1, TYRP1, 877/94 CST6 BAP1_varA, BTG1, ARG1, GJA1, EIF1B, TACSTD2, TYRP1, 8 74/89LTA4H GJA1, ID2, KRT14, ROBO1, RBM23, TRIM29, LTA4H, S100A9 8 70/69DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, CST6 8 72/86 KRT14,ROBO1, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, 8 68/72 AQP3 BAP1_varA,MGP, SPP1, CXCL14, BAP1_varB, CLCA2, 8 81/83 S100A8, BTG1 SAP130, ARG1,KRT6B, GJA1, EIF1B, ID2, EIF1B, S100A9, 8 84/81 CRABP2 MGP, SAP130,GJA1, ID2, S100A9, ROBO1, AQP3, LTA4H 8 65/69 SAP130, ARG1, KRT6B,EIF1B, S100A9, KRT14, ROBO1, 12 91/97 RBM23, TRIM29, AQP3, TYRP1, CST6GJA1, PPL, ROBO1, MGP, TRIM29, AQP3, RBM23, 12 93/94 TACSTD2, TYRP1,KRT6B, EIF1B, DSC1 CRABP2, TYRP1, PPL, EIF1B, SPRR1B, DSC1, GJA1, 1293/89 AQP3, MGP, RBM23, CLCA2, TRIM29 RBM23, TACSTD2, CRABP2, PPL, GJA1,SPP1, CXCL14, 12 91/89 EIF1B, AQP3, MGP, LTA4H, KRT6B BAP1_varA, SPP1,BAP1_varB, S100A8, SAP130, KRT6B, ID2, 12 86/83 S100A9, ROBO1, TACSTD2BAP1_varA, MGP, S100A8, BTG1, ARG1, S100A9, KRT14, 12 75/75 ROBO1,SPRR1B, TRIM29, AQP3, LTA4H SPP1, BAP1_varB, CLCA2, SAP130, GJA1,S100A9, RBM23, 12 84/89 SPRR1B, TYRP1, BTG1, KRT6B EIF1B, S100A9,CRABP2, KRT14, ROBO1, RBM23, TRIM29, 12 82/89 AQP3, TYRP1, PPL, LTA4H,CST6 CXCL14, BAP1_varB, CLCA2, S100A8, BTG1, SAP130, 12 79/92 S100A9,CRABP2, KRT14, ROBO1, RMB23, TACSTD2 MGP, CLCA2, S100A8, ARG1, GJA1,ID2, S100A9, ROBO1, 12 70/78 RBM23, SPRR1B, TRIM29, AQP3 S100A8,TACSTD2, BAP1_varA, KRT6B, EIF1B, TRIM29, 14 91/89 TYRP1, CST6, PPL,RBM23, AQP3, GJA1, SPRR1B, ARG1 BAP1_varB, CLCA2, BTG1, SAP130, GJA1,ID2, S100A9, 14 86/89 CRABP2, RBM23, TACSTD2, DSC1, LTA4H, SPP1, KRT6BSPP1, CLCA2, S100A8, SAP130, ARG1, ID2, EIF1B, S100A9, 14 86/97 KRT14,ROBO1, DSC1, TRIM29, TYRP1, LTA4H CXCL14, BAP1_varB, S100A8, BTG1, ARG1,KRT6B, ID2, 14 89/82 EIF1B, CRABP2, KRT14, RBM23, TACSTD2, SPRR1B,TRIM29 BAP1_varA, MGP, SPP1, CXCL14, BAP1_varB, CLCA2, 14 84/86 S100A8,BTG1, SAP130, ARG1, KRT6B, GJA1, ID1, EIF1B S100A9, CRABP2, KRT14,ROBO1, RBM23, TACSTD2, DSC1, 14 82/78 SPRR1B, TRIM29, AQP3, TYRP1, PPL,LTA4H, CST6 BTG1, SAP130, ARG1, GJA1, EIF1B, CRABP2, ROBO1, 14 84/86RBM23, TACSTD2, DSC1, SPRR1B, AQP3, PPL, CST6 BAP1_varA, MGP, BAP1_varB,CLCA2, BTG1, SAP130, GJA1, 14 72/72 ID2, S100A9, CRABP2, RBM23, TACSTD2,DSC1, LTA4H MGP, CXCL14, S100A8, BTG1, ARG1, GJA1, ID2, S100A9, 14 82/86KRT14, ROBO1, TACSTD2, SPRR1B, TRIM29, TYRP1 S100A8, BTG1, SAP130,EIF1B, S100A9, CRABP2, KRT14, 14 79/86 DSC1, SPRR1B, TRIM29, AQP3, CST6,BAP1_varB, LTA4H CST6, KRT6B, LTA4H, CLCA2, CRABP2, TRIM29, 18 91/89CXCL14, PPL, ARG1, RBM23, GJA1, AQP3, TYRP1, SPP1, DSC1, TACSTD2, EIF1B,BAP1_varA MGP, CXCL14, CLCA2, BTG1, ARG1, GJA1, EIF1B, CRABP2, 18 81/92ROBO1, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, CST6 MGP,BAP1_varB, CLCA2, S100A8, BTG1, KRT6B, GJA1, ID2, 18  81/100 EIF1B,S100A9, ROBO1, RBM23, TACSTD2, AQP3, TYRP1, PPL, LTA4H, SPRR1B

Example 6 28-Gene Signature for Cutaneous Melanoma Predicts AggressiveTumor Progression to SLN, Distant Lymph Nodes, in Transit Metastasis,Distant Metastasis, and Local Recurrence

A total of 29 stage III, sentinel lymph node (SNL) positive, sampleswere included in the study population (17 in the training set and 12 inthe validation set). These results are shown below in Table 4. RBMprediction of risk for the stage III samples resulted in 25 of 29 (86%)cases classified as high risk, class 2. Metastasis beyond the SLN wasdocumented for 23 of the 29 cases, and 21 of those were predicted to beclass 2 by the algorithm. Metastatic events localized to distant lymphnodes were documents for 13 cases, 12 (92%) of which were accuratelypredicted to be class 2. Similarly, 6 of 7 (86%) of in transitmetastasis cases were assigned to class 2, indicating that the 28-genepredictive signature accurately predicts all aggressive cutaneousmelanoma tumor types, including SLN positive, distant nodal, in transit,distant visceral metastases and local recurrence.

TABLE 4 Accuracy of the CM 28-gene signature for predicting metastaticrisk in SLN, distant lymph nodes, in transit metastasis, and locallyrecurrent disease. Training set Validation set called called total class2 total class 2 SLN+ (stage III) 17 14 (82%) 12 11 (92%) samplesdocumented met 12 11 (92%) 11 10 (91%) event no documented met  5  3(60%)  1  1 (100%) event distant lymph node  6 (2 SLN+)  6 (100%)  7 (5SLN+)  6 (86%) metastasis in transit metastasis  3  2 (66%)  4  4 (100%)local recurrence  9  4* (44%)  5**  3 (60%) *2/4 LR have knownmetastatic event (both called class 2) **1/5 LR is stage III (SLN+) -called class 2

Example 7 Statistical Comparison of GEP Signature to Common CMPrognostic Factors

Cox univariate and multivariate regression analysis was performed forthe 104 sample validation cohort following prediction of metastatic risk(Tables 5 and 6). GEP was compared to the individual prognostic factorscomprising AJCC pathologic stage, including Breslow thickness, mitoticrate, and ulceration status, and was also compared directly to AJCCstage. AJCC stage was analyzed as known in the art as Stage IA, IB, IIAvs. IIB, IIC, III, IV for the complete validation set, and as Stage IA,IB, IIA vs. IIB, IIC for analysis that included only Stage I and II.Thus, analysis was performed with inclusion of all validation set cases(Table 5), or with inclusion of only stage I and stage II cases (Table6). Cox univariate analysis resulted in significant correlation withmetastasis for all factors except mitotic rate regardless of whether allvalidation set cases were included, or whether inclusion was restrictedto stage I and II cases only. Comparison of GEP to Breslow thickness,ulceration, and mitotic rate showed that GEP and ulceration weresignificantly correlated to metastatic risk in a multivariate analysis(HR=5.0 and 4.2, p=0.011 and 0.001, respectively). Direct comparison toAJCC stage by multivariate analysis indicates that both factors aresignificantly correlated to metastasis (GEP HR=7.5, p=0.001; AJCCHR=8.5, p<0.0001). These results indicate that the 28-gene signature isable to predict metastasis independently of AJCC TNM staging and otherprognostic factors.

TABLE 5 Univariate and multivariate analysis of cutaneous melanomaprognostic factors with inclusion of cases from all AJCC stages.Univariate Multivariate Variable HR 95% CI p-value HR 95% CI p-valueBreslow 138.5  3.3-5754 0.009 13.1  0.2-972.5 0.243 Ulceration 13.45.9-30.5 4.00E−07 4.2 1.76-10.1  0.001 Mitotic 2.8 0.9-9.3  0.089 2.00.6-6.9  0.288 GEP test 20.3 7.0-58.9 5.13E−07 5.0 1.4-17.4 0.011 AJCC21 8.6-51.4 3.77E−07 8.5 3.2-22.8 2.23E−05 GEP test 20.3 7.0-58.95.13E−07 7.5 2.2-25.2 0.001

An important clinical application of the GEP test is to identifypatients who are staged as low risk (clinical stage I and II) formetastasis, but who are at actual high risk for metastasis based uponthe genomic signature of their CM. The Cox univariate and multivariateregression analysis was performed for those patients in the 104 samplevalidation cohort following prediction of metastatic risk (Table 6) whohad clinical stage I and II CM only. Again, GEP was compared to theelements comprising AJCC pathologic stage: Breslow thickness, mitoticrate, and ulceration status, and directly to AJCC stage. Underunivariate analysis, Breslow's thickness, ulceration status and GEPClass 2 were significant (p<0.0003). However, under multivariateanalysis, GEP Class 2, Breslow thickness and ulceration were significant(Hazard ratio=6.1, 20, and 3.9, respectively; p=0.02, 0.028, and 0.011,respectively). Univariate analyses for comparison of AJCC stage to GEPClass 2 showed both to be statistically significant, but the GEP Class 2variable had a greater Hazard ratio than AJCC stage (20.3 vs. 15.2,respectively). Multivariate analyses demonstrated that both factors wereindependent of each other (p=0.002) but the Hazard Ratio for the GEPClass 2 was again greater than that of AJCC stage (9.6 vs. 5.4).Together, these two result tables (Table 5 and 6) indicate that the28-gene signature predicts metastasis independently of AJCC TNM stagingand other prognostic factors in patients of all stage (I, II, III andIV).

TABLE 6 Univariate and multivariate analysis of cutaneous melanomaprognostic factors with inclusion of cases from AJCC stages I and II.Univariate Multivariate Variable HR 95% CI p-value HR 95% CI p-valueBreslow 165.6 10.7-2559  0.0003 20 1.4-303  0.028 Ulceration 13.14.8-35.6 8.1E−07 3.9 1.4-10.9 0.011 Mitotic 1.7 0.5-5.8  0.407  1.40.4-5.2  0.595 GEP test 20.3 5.8-70.8 2.9E−06 6.1 1.3-27.6 0.020 AJCC15.2 5.8-39.7 6.0E−07 5.4 1.8-15.7 0.002 GEP test 20.3 5.8-70.8 2.9E−069.6 2.3-39.5 0.002

Example 8 28-Gene Signature for Cutaneous Melanoma Predicts Metastasisin Superficial Spreading and Nodular Growth Patterns

Of the four growth patterns described for CM tumors, superficialspreading is the most prevalent, and biologically distinct from nodularmelanomas.¹³ Radial basis machine analysis of the superficial spreadingtumors within the cohort (n=128) resulted in a highly accurateclassification of metastatic and non-metastatic tumors (FIG. 5A). K-MMFS analysis shows that cases predicted to be class 1 have a 100% 5-yearmetastasis free rate, while high risk class 2 cases have only a 5% MFSrate. Additionally, an ROC=1.00 reflects 100% accuracy of the predictionfor superficial spreading tumors. The genetic signature is also able topredict metastatic potential for nodular type CM tumors (FIG. 5B). K-MMFS rate is 81% for class 1 cases, compared to 7% for class 2 cases(p<0.0001). Accuracy of the predicted model was high, as evidenced byROC=0.9323, accuracy of 82%, and sensitivity of 81%. These resultsindicate that the genetic signature has high accuracy for predictingsuperficial spreading disease and lower accuracy for predicting nodulardisease.

Example 9 Trimodal and Linear Methods for Predicting Metastatic RiskAccording to GEP

While a bimodal (low risk Class 1 vs high risk Class 2) may be thepreferred clinical reporting output, additional analysis suggest thatthe tri-modal or linear approach may be clinically appropriate. Analysisusing a trimodal approach demonstrated a stratification outcome relatedto metastatic risk. Specifically, development of a Class A (low risk),Class B (medium risk) and Class C (high risk) trimodal output shows agraduated increase in metastatic risk (FIG. 6). Analysis shows 5-yr MFSfor the independent 104 sample validation set is 98% for class Acompared to 79% for class B and 30% for class C (p<0.0001), with a 64%rate in the overall cohort, reflecting clinically valuable models.

Probability of the metastatic risk prediction is based upon thecoefficients of the 28 variables (genes) and reported as a value between0 and 1. For the two-class analysis, cases with a probability scorebetween 0 and 0.5 are designated low risk, class 1 cases. Conversely,cases with a probability score between 0.5 and 1.0 are classified asclass 2, high risk. The linear output of the probability score can alsobe used directly to assess the risk associated with the geneticsignature of particular CM tumor. As shown in FIG. 7, 5 year MFS ofcases in the 104 sample validation cohort is strongly correlated withthe probability of metastasis. Assessment of all cases with aprobability of metastasis lower than 0.299 shows that none of thosecases has had a documented metastatic event to date, and 5 year MFS is100%. Four of the 29 cases that fall between the probability scores of0.3 and 0.499 have evidence of a metastatic event, and 5 year MFS forthis group is greater than 92%. Those cases with probability scoresbetween 0.5 and 1.0 have a significant reduction in 5 year MFS. Asillustrated by curves F-J in FIG. 7, 0.1 increment increases in theprobability score lead to corresponding reductions in 5 year MFS from50% to 45%, 33%, 25%, and 10%. Thus, the utility of the 28 genepredictive signature lies in both the class assignment of metastaticrisk and the linear correlation of probability with metastatic outcome.

Example 10 Prediction of Distant Metastasis Free Survival (DMFS) andOverall Survival (OS) Using the GEP Prognostic Signature

Initial validation studies to prove the prognostic accuracy of the28-gene signature and clinically useful training set used metastasisfree survival (MFS) as the endpoint for data analysis. MFS reflects theprogression of disease to either regional (including the sentinel lymphnode) and/or distant sites of melanoma spread. Evaluating the endpointsof DMFS (time to spread beyond the basin of the sentinel lymph node) andOS (time to death) indicates that the GEP signature has accurateprognostic capabilities for predicting both endpoints (FIG. 8).Kaplan-Meier analysis was performed for compare MFS, DMFS and OS in tworelated validation sets, the first consisting of 104 CM cases thatincludes Stage I-IV melanomas (FIG. 8A-C), and a second smaller subsetof 78 CM cases that includes only Stage I and Stage II melanomas (FIG.8D-F).

DMFS was determined for the GEP signature, and the results were comparedto Breslow thickness, ulceration and mitotic rate, three clinicalT-factors commonly assessed to determine AJCC stage for each CM patient(FIG. 9). DMFS is grouped by GEP class assignment (FIG. 9A), Breslowthickness of greater or less than 0.75 mm (FIG. 9B), presence or absenceof ulceration (FIG. 9C), or mitotic rate of greater or less than 1/mm²(FIG. 9D). The comparison indicates that the GEP signature hasprognostic accuracy better than, or comparable to, the clinical factorscurrently recommended for the clinical assessment of CM patient risk.

Example 11 Comparison of Prognostic GEP Signature to Sentinel Lymph NodeBiopsy for Predicting Regional and Distant Metastasis

A total of 217 melanoma tumor specimens from patients undergoingsentinel lymph node biopsy (SLNB) had gene expression profilingperformed with the 28-gene signature to classify the cases according torisk. A total of 58 patients had a positive SLNB result while 159patients had a negative SLNB result (Table 7). A total of 37 of 58 (64%)SLNB positive patients developed metastasis and 19 of the 58 (33%)patients died of their disease. Among the SLNB negative patients 70 of159 (44%) developed metastasis and 46 ultimately died of their disease.The AJCC staging parameters of the SLNB positive and negative patientsis summarized in Table 7. The PPV (positive predictive value) of SLNBfor metastasis was 64% while the NPV (negative predictive value) of SLNBfor metastasis was 56% (Table 8).

TABLE 7 Baseline demographics for patients in study cohort who underwenta SLNB procedure. SLN positive SLN negative Clinical Characteristics (n= 58) (n = 159) Median Age (range) 57 (23-94) 63 (31-89)Regional/distant mets 37 70 AJCC Stage I n/a 46 II n/a 112 III 58 0 IVn/a 1 Breslow Thickness Median (range) 4.0 (0.8-16) 2.3 (0.4-14) ≦1 mm 427 >1 mm 51 132 Mitotic Index <1/mm2 2 44 ≧1/mm2 48 100 Ulcerationabsent 20 93 present 33 58

TABLE 8 Positive predictive and negative predictive values for SLNB andGEP prognostic tools. Prognostic Tool PPV, % (95% CI) NPV, % (95% CI)SLNB 64 (50-75) 56 (48-64) GEP 65 (56-72) 79 (68-87)

FIG. 10 shows the KM curves for metastasis free survival (MFS) andoverall survival (OS) for the SLNB negative and the SLNB positivepatients. The respective p-values for the difference between the curvesfor the SLNB positive and SLNB negative patients was <0.0001 and <0.006.

Among the 217 melanoma patients, 141 had a high risk Class 2 GEP resultwhile 76 had a low risk Class 1 result. Ninety-one of the 141 Class(65%) 2 patients developed metastatic disease while 55 died of disease.Sixteen of 76 (21%) Class 1 patients developed metastatic disease and 10of 76 (13%) died of disease. The PPV of the GEP test for metastasis was65% and the NPV was 79%. FIG. 11 shows the KM curve for MFS and OS forthe GEP classification of patients. The respective p-values for thedifference between the curves for the GEP Class 1 and Class 2 patientswas <0.0001 for both analyses. The OS at 5 years for all SLNB positivepatients was 62% compared to 54% for cases with a Class 2 GEP score. TheOS at 5 years for all SLNB negative cases was 70%, compared to 89% forall cases with a Class 1 GEP score.

There was a statistically significant difference in univariate andmultivariate analysis of a SLNB positive result (compared to SLNBnegative) and the GEP Class 2 score (compared to GEP Class 1) whencomparing the metastatic and non-metastatic groups with Cox regression(Table 9). The respective p-values for the multivariate analysis were<0.008 and <3.1×10⁻⁵. Likewise both SLNB result and the GEP score werestatistically significant when comparing cases with death of diseaseversus all others with Cox regression analysis in univariate analysis.The respective p-values being <0.007 and <1.9×10⁻⁶. Conversely, inmultivariate analysis only the GEP score was statistically significantwith a p-value of <5.2×10⁻⁶.

TABLE 9 Positive predictive and negative predictive values for SLNB andGEP prognostic tools. Vari- Univariate Multivariate able HR 95% CIp-value HR 95% CI p-value MFS GEP 5.5 3.2-9.4 4.0E−07 4.9 2.9-8.63.1E−05 Class 2 SLN+ 2.4 1.6-3.6 2.6E−05 1.7 1.2-2.6 0.008 OS GEP 5.4 2.7-10.8 1.9E−06 5.1  2.5-10.2 5.2E−06 Class 2 SLN+ 2.1 1.2-3.6 0.0071.6 0.9-2.8 0.092

FIG. 12 illustrates that using GEP classification in combination withSLNB status further improves prognostication. The overall 5-year MFS and5-year OS for Class 1 patients is 79% and 89%, respectively, and forSLNB negative patients is 55% and 70%, respectively. The 31% of patientswith a GEP Class 1 and a negative SLNB result had an overall 5-year MFSand 5-year OS of 83% and 91%, respectively. The overall 5-year MFS and 5year OS for GEP Class 2 patients is 34% and 54% respectively and forSLNB positive patients is 37% and 62%. The 23% of patients with a GEPClass 2 and a positive SLNB had an overall 5-year MFS and 5 year OS of33% and 57%. For the 42% of patients with a GEP Class 2 but negativeSLNB result, the 5-year MFS and 5-year OS were 35% and 54%,respectively, similar to those patients who were Class 2 with a positiveSLNB result. Only a small cohort of 9 patients (representing 4%) with aClass 1 GEP score, but positive SLNB result, was identified. The overall5-year MFS and 5-year OS for this group was 53% and 78%, respectively.The respective p-values for the difference between the four combined GEPand SLNB groups was <0.0001 for both MFS and OS analyses.

Prediction of MFS and DMFS when combining GEP Class with SLNB outcome isshown in FIG. 13. Cases with Class 1/SLN− results continue to exhibitthe lowest risk of distant metastasis, with 86% 5-year DMFS rates.Conversely, Class 2/SLN+ cases have the highest risk of developingdistant disease, and have a 42% DMFS rate. As shown in FIG. 14, addingthe GEP signature to AJCC staging provided a surgical yield of 35%(49/141) compared to 27% (58/217) with AJCC staging alone. A prognosticadvantage was also observed for the GEP, as SLNB procedure (C)identified 37/107 cases (sensitivity=35%) with a documented metastaticevent, while adding GEP to SLNB (D) identified 91/107 cases(sensitivity=85%) with a documented metastatic event.

While the invention has been described in terms of various embodiments,it is understood that variations and modifications will occur to thoseskilled in the art. Therefore, it is intended that the appended claimscover all such equivalent variations that come within the scope of theinvention as claimed. In addition, the section headings used herein arefor organizational purposes only and are not to be construed as limitingthe subject matter described.

Each embodiment herein described may be combined with any otherembodiment or embodiments unless clearly indicated to the contrary. Inparticular, any feature or embodiment indicated as being preferred oradvantageous may be combined with any other feature or features orembodiment or embodiments indicated as being preferred or advantageous,unless clearly indicated to the contrary.

All references cited in this application are expressly incorporated byreference herein.

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What is claimed is:
 1. A method for predicting risk of metastasis,overall survival, or both, in a patient with a primary cutaneousmelanoma tumor, the method comprising: (a) measuring gene expressionlevels of at least eight genes selected from the group consisting ofBAP1_varA, BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130,ARG1, KRT6B, GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBOT, RBM23,TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6, in asample of the primary cutaneous melanoma tumor, wherein measuring geneexpression levels of the at least eight genes comprises measurement of alevel of fluorescence by a sequence detection system following RT-PCR ofthe at least eight genes; (b) determining a patient gene-expressionprofile signature comprising the gene expression levels of the at leasteight genes; (c) comparing the patient gene-expression profile signatureto a gene-expression profile of a predictive training set; and (d)providing an indication as to a risk of metastasis, overall survival orboth for the primary cutaneous melanoma tumor when the patient geneexpression profile indicates that the expression levels of at the leasteight genes are altered in a predictive manner as compared to the geneexpression profile of the predictive training set.
 2. The method ofclaim 1, wherein the risk of metastasis for the primary cutaneousmelanoma tumor is classified from a low risk of metastasis to a highrisk of metastasis.
 3. The method of claim 2, wherein class 1 indicatesa low risk of metastasis, class 2 indicates a high risk of metastasis,class A indicates a low risk of metastasis, class B indicates anintermediate risk of metastasis and class C indicates a high risk ofmetastasis.
 4. The method of claim 1, further comprising determiningthat the primary cutaneous melanoma tumor has an increased risk ofmetastasis or decreased overall survival by combining with at least oneof TNM (Tumor-Node-Metastasis) status, clinical staging set by AmericanJoint Committee on Cancer (AJCC) to stage the primary cutaneous melanomatumor, and sentinel lymph node biopsy status.
 5. The method of claim 1,wherein a sentinel lymph node biopsy was performed in the patient fromwhich the primary cutaneous melanoma tumor was separately obtained. 6.The method of claim 5, wherein the sentinel lymph node biopsy wasnegative.
 7. The method of claim 1, wherein the at least eight genes areKRT6B, GJA1, AQP3, TRIM29, TYRP1, RBM23, MGP and EIF1B.
 8. The method ofclaim 1, wherein the at least eight genes are SAP130, ARG1, KRT6B,EIF1B, S100A9, KRT14, ROBOT, RBM23, TRIM29, AQP3, TYRP1 and CST6.
 9. Themethod of claim 1, wherein the at least eight genes are GJA1, PPL,ROBOT, MGP, TRIM29, AQP3, RBM23, TACSTD2, TYRP1, KRT6B, EIF1B and DSC1.10. The method of claim 1, wherein the at least eight genes are CRABP2,TYRP1, PPL, EIF1B, SPRR1B, DSC1, GJA1, AQP3, MGP, RBM23, CLCA2 andTRIM29.
 11. The method of claim 1, wherein the at least eight genes areRBM23, TACSTD2, CRABP2, PPL, GJA1, SPP1, CXCL14, EIF1B, AQP3, MGP, LTA4Hand KRT6B.
 12. The method of claim 1, wherein the at least eight genesare S100A8, TACSTD2, BAP1_varA, KRT6B, EIF1B, TRIM29, TYRP1, CST6, PPL,RBM23, AQP3, GJA1, SPRR1B and ARG1.
 13. The method of claim 1, whereinthe at least eight genes are CST6, KRT6B, LTA4H, CLCA2, CRABP2, TRIM29,CXCL14, PPL, ARG1, RBM23, GJA1, AQP3, TYRP1, SPP1, DSC1, TACSTD2, EIF1B,and BAP1_varA.
 14. The method of claim 1, wherein the primary cutaneousmelanoma tumor is taken from a formalin-fixed, paraffin embedded widelocal excision sample.
 15. The method of claim 1, wherein the primarycutaneous melanoma tumor is taken from formalin-fixed, paraffin embeddedbiopsy sample selected from a punch biopsy, a shave biopsy and anotherbiopsy method.
 16. A method of treating cutaneous melanoma in a patient,the method comprising: (a) measuring gene expression levels of at leasteight genes selected from the group consisting of BAP1_varA, BAP1_varB,MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2,EIF1B, S100A9, CRABP2, KRT14, ROBOT, RBM23, TACSTD2, DSC1, SPRR1B,TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6 in a sample of a primarycutaneous melanoma tumor in the patient, wherein measuring geneexpression levels of the at least eight genes comprises determining alevel of fluorescence by a sequence detection system following RT-PCR ofthe at least eight genes; (b) determining a patient gene-expressionprofile signature comprising the gene expression levels of the at leasteight genes; (c) comparing the patient gene-expression profile signatureto a gene-expression profile of a predictive training set; (d) making adetermination as to whether the patient gene-expression profilesignature of the at least eight genes is altered in a predictive manner;and (e) administering an aggressive cancer treatment regimen to thepatient when the determination is made in the affirmative that thepatient has a primary cutaneous melanoma tumor with an increased risk ofmetastasis or decreased overall survival.
 17. A method of treatingcutaneous melanoma in a patient, the method comprising: (a) measuringgene expression levels of at least eight genes selected from the groupconsisting of BAP1_varA, BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8,BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B, S100A9, CRABP2, KRT14,ROBOT, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H,and CST6 in a sample of a primary cutaneous melanoma tumor in thepatient, wherein measuring gene expression levels of the at least eightgenes comprises determining a level of fluorescence by a sequencedetection system following RT-PCR of the at least eight genes; (b)determining a patient gene-expression profile signature comprising thegene expression levels of the at least eight genes; (c) comparing thepatient gene-expression profile signature to a gene-expression profileof a predictive training set; (d) making a determination as to whetherthe patient gene-expression profile signature of the at least eightgenes is altered in a predictive manner; and (e) performing a sentinellymph node biopsy (SLNB) on the patient when the determination is madein the affirmative that the patient has a primary cutaneous melanomatumor with an increased risk of metastasis or decreased overallsurvival.
 18. The method of claim 17, further comprising determiningthat the primary cutaneous melanoma tumor has an increased risk ofmetastasis or decreased overall survival by combining with TNM status,AJCC clinical staging to stage the primary cutaneous melanoma tumor, orboth.